Computationally Efficient Spatio-Temporal Dynamic Texture Recognition for Volatile Organic Compound (VOC) Leakage Detection in Industrial Plants

摘要

In this article, we present a computationally efficient algorithm to detect Volatile Organic Compounds (VOC) leaking out of components used in chemical processes in petrochemical refineries and chemical plants. A leaking VOC plume from a damaged component appears as a dynamic dark cloud in infrared videos. We describe a two-stage deep neural network structure, taking advantage of both spatial and temporal structure of the dynamic texture regions created by the leaking VOC plume. We first detect moving pixels which are darker then their neighboring pixels. We extract one-dimensional (1-D) signals representing the temporal history of such pixels from video and feed the 1-D signals to a 1-D convolutional neural network. If those pixels are near the edge of a VOC plume, their 1-D temporal signals exhibit high-frequency behavior. The neural network generates high probability estimates for such pixels. If 1-D neural network generates high confidence values, final decision is reached using a deep convolutional neural network (CNN) which processes image frames. The overall structure is computationally efficient because the spatio-temporal CNN does not process all of the image frames of the captured video. Experimental results are presented.